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      "name": "Section 2.2.7 Stock Market Returns are Not Normally Distributed",
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            "Installing yfinance and arch and getting the data...\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
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            "text/plain": [
              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "1957-03-04    44.060001    44.060001    44.060001    44.060001    44.060001   \n",
              "1957-03-05    44.220001    44.220001    44.220001    44.220001    44.220001   \n",
              "1957-03-06    44.230000    44.230000    44.230000    44.230000    44.230000   \n",
              "1957-03-07    44.209999    44.209999    44.209999    44.209999    44.209999   \n",
              "1957-03-08    44.070000    44.070000    44.070000    44.070000    44.070000   \n",
              "...                 ...          ...          ...          ...          ...   \n",
              "2022-03-23  4493.100098  4501.069824  4455.810059  4456.240234  4456.240234   \n",
              "2022-03-24  4469.979980  4520.580078  4465.169922  4520.160156  4520.160156   \n",
              "2022-03-25  4522.910156  4546.029785  4501.069824  4543.060059  4543.060059   \n",
              "2022-03-28  4541.089844  4552.750000  4517.689941  4540.910156  4540.910156   \n",
              "2022-03-29  4602.859863  4627.629883  4589.660156  4611.629883  4611.629883   \n",
              "\n",
              "                  Volume    Return  \n",
              "Date                                \n",
              "1957-03-04  1.890000e+06  0.731595  \n",
              "1957-03-05  1.860000e+06  0.363141  \n",
              "1957-03-06  1.840000e+06  0.022610  \n",
              "1957-03-07  1.830000e+06 -0.045219  \n",
              "1957-03-08  1.630000e+06 -0.316669  \n",
              "...                  ...       ...  \n",
              "2022-03-23  4.014360e+09 -1.227270  \n",
              "2022-03-24  3.573430e+09  1.434391  \n",
              "2022-03-25  3.577520e+09  0.506617  \n",
              "2022-03-28  3.696850e+09 -0.047323  \n",
              "2022-03-29  1.039370e+09  1.557391  \n",
              "\n",
              "[16382 rows x 7 columns]"
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              "      <th>Open</th>\n",
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              "      <th>Date</th>\n",
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              "      <th>1957-03-04</th>\n",
              "      <td>44.060001</td>\n",
              "      <td>44.060001</td>\n",
              "      <td>44.060001</td>\n",
              "      <td>44.060001</td>\n",
              "      <td>44.060001</td>\n",
              "      <td>1.890000e+06</td>\n",
              "      <td>0.731595</td>\n",
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              "    <tr>\n",
              "      <th>1957-03-05</th>\n",
              "      <td>44.220001</td>\n",
              "      <td>44.220001</td>\n",
              "      <td>44.220001</td>\n",
              "      <td>44.220001</td>\n",
              "      <td>44.220001</td>\n",
              "      <td>1.860000e+06</td>\n",
              "      <td>0.363141</td>\n",
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              "    <tr>\n",
              "      <th>1957-03-06</th>\n",
              "      <td>44.230000</td>\n",
              "      <td>44.230000</td>\n",
              "      <td>44.230000</td>\n",
              "      <td>44.230000</td>\n",
              "      <td>44.230000</td>\n",
              "      <td>1.840000e+06</td>\n",
              "      <td>0.022610</td>\n",
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              "    <tr>\n",
              "      <th>1957-03-07</th>\n",
              "      <td>44.209999</td>\n",
              "      <td>44.209999</td>\n",
              "      <td>44.209999</td>\n",
              "      <td>44.209999</td>\n",
              "      <td>44.209999</td>\n",
              "      <td>1.830000e+06</td>\n",
              "      <td>-0.045219</td>\n",
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              "    <tr>\n",
              "      <th>1957-03-08</th>\n",
              "      <td>44.070000</td>\n",
              "      <td>44.070000</td>\n",
              "      <td>44.070000</td>\n",
              "      <td>44.070000</td>\n",
              "      <td>44.070000</td>\n",
              "      <td>1.630000e+06</td>\n",
              "      <td>-0.316669</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>2022-03-23</th>\n",
              "      <td>4493.100098</td>\n",
              "      <td>4501.069824</td>\n",
              "      <td>4455.810059</td>\n",
              "      <td>4456.240234</td>\n",
              "      <td>4456.240234</td>\n",
              "      <td>4.014360e+09</td>\n",
              "      <td>-1.227270</td>\n",
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              "      <th>2022-03-24</th>\n",
              "      <td>4469.979980</td>\n",
              "      <td>4520.580078</td>\n",
              "      <td>4465.169922</td>\n",
              "      <td>4520.160156</td>\n",
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              "      <th>2022-03-25</th>\n",
              "      <td>4522.910156</td>\n",
              "      <td>4546.029785</td>\n",
              "      <td>4501.069824</td>\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "print(\"Installing yfinance and arch and getting the data...\")\n",
        "!pip install arch 1>/dev/null\n",
        "!pip install yfinance 1>/dev/null\n",
        "from yfinance import download\n",
        "import pandas as pd\n",
        "import numpy as np ;\n",
        "import matplotlib.pyplot as pl\n",
        "from statsmodels.base.model import GenericLikelihoodModel\n",
        "from datetime import datetime\n",
        "zero,one,two,five,ten,hundred=0e0,1e0,2e0,5e0,1e1,1e2 # some friendly numbers\n",
        "half,GoldenRatio=one/two,(one+np.sqrt(five))/two\n",
        "\n",
        "# get the daily returns of the S&P 500 \n",
        "SPX=download('^GSPC','1957-03-01').dropna()\n",
        "SPX['Return']=SPX['Adj Close'].pct_change()*hundred\n",
        "SPX.index=pd.DatetimeIndex(SPX.index).to_period('D')\n",
        "SPX.dropna(inplace=True)\n",
        "SPX.loc[SPX[\"Volume\"]==0,\"Volume\"]=np.nan\n",
        "SPX"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit a GARCH model using arch package\n",
        "print(\"Installing ARCH and fitting model with GED innovations.\")\n",
        "from arch.univariate import ConstantMean, GARCH, GeneralizedError\n",
        "\n",
        "model = ConstantMean(SPX[\"Return\"])\n",
        "model.volatility = GARCH(1, 0, 1)\n",
        "model.distribution = GeneralizedError()\n",
        "fit=model.fit(update_freq=0)\n",
        "print(fit.summary())\n",
        "SPX[\"Volatility\"]=fit.conditional_volatility\n",
        "SPX[\"Innovations\"]=SPX[\"Return\"]/SPX[\"Volatility\"]\n",
        "\n",
        "# fit normal pdf to plot\n",
        "from scipy.stats import gennorm as density,jarque_bera\n",
        "params=density.fit(SPX[\"Innovations\"])\n",
        "fitted=density(*params)\n",
        "print(\"Fitted GED distribution to residuals: %g,%g,%g\" % (params[1],params[2]/np.power(two,one/params[0]),one/params[0]))\n",
        "test=jarque_bera(SPX[\"Innovations\"])\n",
        "print(\"Jarque-Bera Test results: %g,%g\" % test)\n",
        "\n",
        "# Figure 2.9\n",
        "figure,plot=pl.subplots(figsize=(ten*GoldenRatio,ten))\n",
        "counts,bins,patches=plot.hist(SPX[\"Innovations\"],bins=np.linspace(-five,five,100),label='Data')\n",
        "plot.plot(bins,fitted.pdf(bins)*(max(bins)-min(bins))/(len(bins)-1)*sum(counts),'-r',label=\"\"\"Entries %d %s to %s\n",
        "Jarque-Bera  %12.6f %12.6f\n",
        "$\\\\hat\\\\mu$            %12.6f\n",
        "$\\\\hat\\\\sigma$            %12.6f \n",
        "$\\\\hat\\\\kappa$            %12.6f\"\"\" % (\n",
        "    sum(counts),min(SPX.index),max(SPX.index),\n",
        "    test[0],test[1],\n",
        "    params[1],\n",
        "    params[2]/np.power(two,one/params[0]),\n",
        "    one/params[0]\n",
        "))\n",
        "plot.axvline(color='black',lw=1)\n",
        "plot.set_xlabel(\"Innovations\",fontsize=14)\n",
        "plot.set_ylabel(\"Frequency\",fontsize=14)\n",
        "figure.suptitle(\"\\nFit of GARCH(1,1) Model to S&P 500 Index\",fontsize=22)\n",
        "plot.set_title(\"Driver: Returns; Distribution: Error; Period: %s to %s\" % (min(SPX.index),max(SPX.index)),fontsize=18)\n",
        "pl.setp(plot.legend(loc='best',fontsize=12).texts,family='monospace');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wNqwBZ61d0v7",
        "outputId": "0ba639e2-288d-4d92-b84b-1d2e367b23a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing ARCH and fitting model with GED innovations.\n",
            "Optimization terminated successfully.    (Exit mode 0)\n",
            "            Current function value: 19569.525340522687\n",
            "            Iterations: 15\n",
            "            Function evaluations: 121\n",
            "            Gradient evaluations: 15\n",
            "                           Constant Mean - GARCH Model Results                            \n",
            "==========================================================================================\n",
            "Dep. Variable:                             Return   R-squared:                       0.000\n",
            "Mean Model:                         Constant Mean   Adj. R-squared:                  0.000\n",
            "Vol Model:                                  GARCH   Log-Likelihood:               -19569.5\n",
            "Distribution:      Generalized Error Distribution   AIC:                           39149.1\n",
            "Method:                        Maximum Likelihood   BIC:                           39187.6\n",
            "                                                    No. Observations:                16382\n",
            "Date:                            Tue, Mar 29 2022   Df Residuals:                    16381\n",
            "Time:                                    16:49:30   Df Model:                            1\n",
            "                                 Mean Model                                 \n",
            "============================================================================\n",
            "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
            "----------------------------------------------------------------------------\n",
            "mu             0.0582  5.033e-03     11.559  6.668e-31 [4.831e-02,6.804e-02]\n",
            "                              Volatility Model                              \n",
            "============================================================================\n",
            "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
            "----------------------------------------------------------------------------\n",
            "omega      8.3582e-03  1.351e-03      6.185  6.216e-10 [5.710e-03,1.101e-02]\n",
            "alpha[1]       0.0904  7.165e-03     12.614  1.761e-36   [7.634e-02,  0.104]\n",
            "beta[1]        0.9034  7.179e-03    125.848      0.000     [  0.889,  0.918]\n",
            "                              Distribution                              \n",
            "========================================================================\n",
            "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
            "------------------------------------------------------------------------\n",
            "nu             1.3561  2.778e-02     48.811      0.000 [  1.302,  1.411]\n",
            "========================================================================\n",
            "\n",
            "Covariance estimator: robust\n",
            "Fitted GED distribution to residuals: 0.0568494,0.637943,0.734698\n",
            "Jarque-Bera Test results: 5740.85,0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1164.98x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "9RZs0eXxxj-s"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}
